{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# K-Means"
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "K-means is used for clustering such as unlabled data. \n",
        "\n\nk-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. (Wikipedia)"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing the libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# fix_yahoo_finance is used to fetch data \n",
        "import fix_yahoo_finance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View columns \n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 downloaded\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>3.85</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.84</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>20548400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>3.98</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>22887200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.01</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>42398300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.19</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>42932100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.23</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>30678700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Open  High   Low  Close  Adj Close    Volume\n",
              "Date                                                    \n",
              "2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n",
              "2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n",
              "2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n",
              "2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n",
              "2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pre-processing Data"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset[['Open','High','Low','Close','Adj Close','Volume']]"
      ],
      "outputs": [],
      "execution_count": 3,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Normalizing over the standard deviation"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "X = dataset.values[:,1:]\n",
        "X = np.nan_to_num(X)\n",
        "Clus_dataSet = StandardScaler().fit_transform(X)\n",
        "Clus_dataSet"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": [
              "array([[-0.63841038, -0.63386523, -0.62754122, -0.62754122, -0.50102319],\n",
              "       [-0.63439667, -0.62549188, -0.6173066 , -0.6173066 , -0.43332499],\n",
              "       [-0.59827322, -0.60246516, -0.59069658, -0.59069658,  0.13143746],\n",
              "       ...,\n",
              "       [ 3.04216769,  2.98760923,  3.12651808,  3.12651808,  2.18790785],\n",
              "       [ 3.37931988,  3.30789014,  3.47244808,  3.47244808,  3.66078372],\n",
              "       [ 4.04158291,  3.71818418,  3.73445438,  3.73445438,  8.31323182]])"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 6,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "k-means Modeling"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans \n",
        "\n",
        "clusterNum = 3\n",
        "k_means = KMeans(init = \"k-means++\", n_clusters = clusterNum, n_init = 12)\n",
        "k_means.fit(X)\n",
        "labels = k_means.labels_\n",
        "print(labels)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 2 ... 1 1 1]\n"
          ]
        }
      ],
      "execution_count": 8,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset[\"Prices\"] = labels\n",
        "dataset.head(5)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Clus_km</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2014-01-02</th>\n",
              "      <td>3.85</td>\n",
              "      <td>3.98</td>\n",
              "      <td>3.84</td>\n",
              "      <td>3.95</td>\n",
              "      <td>3.95</td>\n",
              "      <td>20548400</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-03</th>\n",
              "      <td>3.98</td>\n",
              "      <td>4.00</td>\n",
              "      <td>3.88</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.00</td>\n",
              "      <td>22887200</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-06</th>\n",
              "      <td>4.01</td>\n",
              "      <td>4.18</td>\n",
              "      <td>3.99</td>\n",
              "      <td>4.13</td>\n",
              "      <td>4.13</td>\n",
              "      <td>42398300</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-07</th>\n",
              "      <td>4.19</td>\n",
              "      <td>4.25</td>\n",
              "      <td>4.11</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>42932100</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-01-08</th>\n",
              "      <td>4.23</td>\n",
              "      <td>4.26</td>\n",
              "      <td>4.14</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.18</td>\n",
              "      <td>30678700</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Open  High   Low  Close  Adj Close    Volume  Clus_km\n",
              "Date                                                             \n",
              "2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400        0\n",
              "2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200        0\n",
              "2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300        2\n",
              "2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100        2\n",
              "2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700        0"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 9,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.groupby('Prices').mean()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Clus_km</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4.390013</td>\n",
              "      <td>4.464206</td>\n",
              "      <td>4.314272</td>\n",
              "      <td>4.388585</td>\n",
              "      <td>4.388585</td>\n",
              "      <td>1.871029e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12.799600</td>\n",
              "      <td>13.336200</td>\n",
              "      <td>12.342400</td>\n",
              "      <td>12.841600</td>\n",
              "      <td>12.841600</td>\n",
              "      <td>1.532736e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11.507399</td>\n",
              "      <td>11.748954</td>\n",
              "      <td>11.262198</td>\n",
              "      <td>11.510349</td>\n",
              "      <td>11.510349</td>\n",
              "      <td>6.083450e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Open       High        Low      Close  Adj Close        Volume\n",
              "Clus_km                                                                     \n",
              "0         4.390013   4.464206   4.314272   4.388585   4.388585  1.871029e+07\n",
              "1        12.799600  13.336200  12.342400  12.841600  12.841600  1.532736e+08\n",
              "2        11.507399  11.748954  11.262198  11.510349  11.510349  6.083450e+07"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "area = np.pi * ( X[:, 1])**2  \n",
        "plt.scatter(X[:, 0], X[:, 3], s=area, c=labels.astype(np.float), alpha=0.5)\n",
        "plt.xlabel('Open', fontsize=18)\n",
        "plt.ylabel('Close', fontsize=16)\n",
        "\nplt.show()"
      ],
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 13,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from mpl_toolkits.mplot3d import Axes3D \n",
        "fig = plt.figure(1, figsize=(8, 6))\n",
        "plt.clf()\n",
        "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
        "\n",
        "plt.cla()\n",
        "# plt.ylabel('High', fontsize=18)\n",
        "# plt.xlabel('Open', fontsize=16)\n",
        "# plt.zlabel('Close', fontsize=16)\n",
        "ax.set_xlabel('High')\n",
        "ax.set_ylabel('Open')\n",
        "ax.set_zlabel('Close')\n",
        "\nax.scatter(X[:, 1], X[:, 0], X[:, 3], c= labels.astype(np.float))"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": [
              "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x2637fe3fbe0>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": [
              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\n"
            ],
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": false,
        "outputHidden": false,
        "inputHidden": false
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "pygments_lexer": "ipython3",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "version": "3.5.5",
      "mimetype": "text/x-python",
      "file_extension": ".py",
      "name": "python",
      "nbconvert_exporter": "python"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    },
    "nteract": {
      "version": "0.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}